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Real-Time Monitoring Technology for Built Infrastructure Systems
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Real-Time Monitoring Technology for Built Infrastructure Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 8744

Special Issue Editors

Dr. Vikram Pakrashi

E-Mail Website
Guest Editor
School of Mechanical and Materials Engineering, University College Dublin, 4 Dublin, Ireland
Interests: vibration; energy harvesting; structural health monitoring and control; smart materials and structures; dynamical systems; risk quantification and reliability analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Ekin Ozer

E-Mail Website
Guest Editor
School of Civil Engineering, University College Dublin, D04V1W8 Dublin, Ireland
Interests: bridge engineering; structural health monitoring; system identification; structural dynamics; earthquake engineering; sensor technologies; machine learning; decision analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Q. Feng

E-Mail Website
Guest Editor
Department of Civil Engineering and Engineering Mechanics, Columbia University, New York City, NY 10027, USA
Interests: smart materials and structures; sensors; structural health monitoring; structural control; robotics; safety and sustainability of civil infrastructure systems

Special Issue Information

Dear Colleagues,

Real-time detection of built infrastructure systems is rapidly gaining prominence for traditional (e.g., railways, bridges, pipelines) and emerging (e.g., wind turbines) infrastructure. While damage detection and structural health monitoring remain key questions, detection of other features of interest includes control, repair/rehabilitation, and other lifetime performance measures. This Special Issue addresses new methods, infrastructure demands, feature development, and related implementation around the question of ‘real-time’, in its widest interpretation. The topics include but are not limited to:

  • Recursive methods for real-time detection;
  • Model updating;
  • Digital twinning;
  • Sensor placement strategies;
  • Sensor comparison;
  • Monitoring design;
  • Novel features of interest;
  • Creation of robust detection of markers;
  • Artificial Intelligence;
  • Guidelines of reproducibility and accuracy;
  • Quantification and qualification of uncertainty;
  • Health-monitoring-informed decision support;
  • Surrogate modeling applications;
  • Advanced computer vision.

Dr. Vikram Pakrashi
Dr. Ekin Ozer
Prof. Dr. Maria Q. Feng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • real-time
  • infrastructure
  • recursive
  • railways
  • bridge
  • wind turbines
  • pipelines
  • time-varying
  • time series
  • statistics
  • structural health monitoring
  • damage detection
  • control

Published Papers (5 papers)

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Research

25 pages, 3433 KiB  
Article
Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations
by Susanne Reetz, Taoufik Najeh, Jan Lundberg and Jörn Groos
Sensors 2024, 24(2), 477; https://doi.org/10.3390/s24020477 - 12 Jan 2024
Viewed by 679
Abstract
Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway [...] Read more.
Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway switches are required. A common approach to track superstructure monitoring is to measure the acceleration caused by vehicle track interaction. Local interruptions in the wheel–rail contact, caused for example by local defects or track discontinuities, appear in the data as transient impact events. In this paper, such transient events are investigated in an experimental setup of a railway switch with track-side acceleration sensors, using frequency and waveform analysis. The aim is to understand if and how the origins of these impact events can be distinguished in the data of this experiment, and what the implications for condition monitoring of local track discontinuities and defects with wayside acceleration sensors are in practice. For the same experimental configuration, individual impact events are shown to be reproducible in waveform and frequency content. Nevertheless, with this track-side sensor setup, the different types of track discontinuities and defects (squats, joints, crossing) could not be clearly distinguished using characteristic frequencies or waveforms. Other factors, such as the location of impact event origin relative to the sensor, are shown to have a much stronger influence. The experimental data suggest that filtering the data to narrow frequency bands around certain natural track frequencies could be beneficial for impact event detection in practice, but differentiating between individual impact event origins requires broadband signals. A multi-sensor setup with time-synchronized acceleration sensors distributed over the switch is recommended. Full article
(This article belongs to the Special Issue Real-Time Monitoring Technology for Built Infrastructure Systems)
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18 pages, 4513 KiB  
Article
An Ensemble Approach for Robust Automated Crack Detection and Segmentation in Concrete Structures
by Muhammad Sohaib, Saima Jamil and Jong-Myon Kim
Sensors 2024, 24(1), 257; https://doi.org/10.3390/s24010257 - 01 Jan 2024
Viewed by 1193
Abstract
To prevent potential instability the early detection of cracks is imperative due to the prevalent use of concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as the traditional manual inspection methods are time-consuming. The existing automated concrete [...] Read more.
To prevent potential instability the early detection of cracks is imperative due to the prevalent use of concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as the traditional manual inspection methods are time-consuming. The existing automated concrete crack detection algorithms, despite recent advancements, face challenges in robustness, particularly in precise crack detection amidst complex backgrounds and visual distractions, while also maintaining low inference times. Therefore, this paper introduces a novel ensemble mechanism based on multiple quantized You Only Look Once version 8 (YOLOv8) models for the detection and segmentation of cracks in concrete structures. The proposed model is tested on different concrete crack datasets yielding enhanced segmentation results with at least 89.62% precision and intersection over a union score of 0.88. Moreover, the inference time per image is reduced to 27 milliseconds which is at least a 5% improvement over other models in the comparison. This is achieved by amalgamating the predictions of the trained models to calculate the final segmentation mask. The noteworthy contributions of this work encompass the creation of a model with low inference time, an ensemble mechanism for robust crack segmentation, and the enhancement of the learning capabilities of crack detection models. The fast inference time of the model renders it appropriate for real-time applications, effectively tackling challenges in infrastructure maintenance and safety. Full article
(This article belongs to the Special Issue Real-Time Monitoring Technology for Built Infrastructure Systems)
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23 pages, 10922 KiB  
Article
Applications of Computer Vision-Based Structural Monitoring on Long-Span Bridges in Turkey
by Chuanzhi Dong, Selcuk Bas and Fikret Necati Catbas
Sensors 2023, 23(19), 8161; https://doi.org/10.3390/s23198161 - 29 Sep 2023
Cited by 3 | Viewed by 1904
Abstract
Structural displacement monitoring is one of the major tasks of structural health monitoring and it is a significant challenge for research and engineering practices relating to large-scale civil structures. While computer vision-based structural monitoring has gained traction, current practices largely focus on laboratory [...] Read more.
Structural displacement monitoring is one of the major tasks of structural health monitoring and it is a significant challenge for research and engineering practices relating to large-scale civil structures. While computer vision-based structural monitoring has gained traction, current practices largely focus on laboratory experiments, small-scale structures, or close-range applications. This paper demonstrates its applications on three landmark long-span suspension bridges in Turkey: the First Bosphorus Bridge, the Second Bosphorus Bridge, and the Osman Gazi Bridge, among the longest landmark bridges in the world, with main spans of 1074 m, 1090 m, and 1550 m, respectively. The presented studies achieved non-contact displacement monitoring from a distance of 600 m, 755 m, and 1350 m for the respective bridges. The presented concepts, analysis, and results provide an overview of long-span bridge monitoring using computer vision-based monitoring. The results are assessed with conventional monitoring approaches and finite element analysis based on observed traffic conditions. Both displacements and dynamic frequencies align well with these conventional techniques and finite element analyses. This study also highlights the challenges of computer vision-based structural monitoring of long-span bridges and presents considerations such as the encountered adverse environmental factors, target and algorithm selection, and potential directions of future studies. Full article
(This article belongs to the Special Issue Real-Time Monitoring Technology for Built Infrastructure Systems)
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17 pages, 1454 KiB  
Article
Spiking Neural Networks for Structural Health Monitoring
by George Vathakkattil Joseph and Vikram Pakrashi
Sensors 2022, 22(23), 9245; https://doi.org/10.3390/s22239245 - 28 Nov 2022
Cited by 4 | Viewed by 2691
Abstract
This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks [...] Read more.
This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks can be effectively used to extract cepstral coefficients as features of vibration signals of structures in their operational conditions. We demonstrate that the neural cepstral coefficients extracted by the network can be successfully used for anomaly detection. To address the power efficiency of sensor nodes, related to both processing and transmission, affecting the applicability of the proposed approach, we implement the algorithm on specialised neuromorphic hardware (Intel ® Loihi architecture) and benchmark the results using numerical and experimental data of degradation in the form of stiffness change of a single degree of freedom system excited by Gaussian white noise. The work is expected to open a new direction of SHM applications towards non-Von Neumann computing through a neuromorphic approach. Full article
(This article belongs to the Special Issue Real-Time Monitoring Technology for Built Infrastructure Systems)
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21 pages, 9773 KiB  
Article
NiTi SMA Superelastic Micro Cables: Thermomechanical Behavior and Fatigue Life under Dynamic Loadings
by Paulo C. S. Silva, Estephanie N. D. Grassi, Carlos J. Araújo, João M. P. Q. Delgado and Antonio G. B. Lima
Sensors 2022, 22(20), 8045; https://doi.org/10.3390/s22208045 - 21 Oct 2022
Cited by 3 | Viewed by 1393
Abstract
Shape memory alloy (SMA) micro cables have a wide potential for attenuation of vibrations and structural health monitoring due to energy dissipation. This work evaluates the effect of SMA thermomechanical coupling during dynamic cycling and the fatigue life of NiTi SMA micro cables [...] Read more.
Shape memory alloy (SMA) micro cables have a wide potential for attenuation of vibrations and structural health monitoring due to energy dissipation. This work evaluates the effect of SMA thermomechanical coupling during dynamic cycling and the fatigue life of NiTi SMA micro cables submitted to tensile loadings at frequencies from 0.25 Hz to 10 Hz. The thermomechanical coupling was characterized using a previously developed methodology that identifies the self-heating frequency. When dynamically loaded above this frequency, the micro cable response is dominated by the self-heating, stiffening significantly during cycling. Once above the self-heating frequency, structural and functional fatigues of the micro cable were evaluated as a function of the loading frequency for the failure of each individual wire. All tests were performed on a single wire with equal cross-section area for comparison purposes. We observed that the micro cable’s functional properties regarding energy dissipation capacity decreased throughout the cycles with increasing frequency. Due to the additional friction between the filaments of the micro cable, this dissipation capacity is superior to that of the single wire. Although its fatigue life is shorter, its delayed failure compared to a single wire makes it a more reliable sensor for structural health monitoring. Full article
(This article belongs to the Special Issue Real-Time Monitoring Technology for Built Infrastructure Systems)
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